AMIA Annu Symp Proc - Towards mechanism classifiers: expression-anchored Gene Ontology signature predicts clinical outcome in lung adenocarcinoma patients.

Tópicos

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Resumo

We aim to provide clinically applicable, reproducible, mechanistic interpretations of gene expression changes that lack in gene overlap among predictive gene-signatures. Using a method we recently developed, Functional Analysis of Individual Microarray Expression (FAIME), we provide evidence that Gene Ontology-anchored signatures (GO-signatures) show reliable prognosis in lung cancer. In order to demonstrate the biological congruence and reproducibility of FAIME-derived mechanism classifiers, we chose a disease where gene expression classifiers signatures alone had failed to significantly stratify a larger collection of samples and that exhibited poor or no genetic overlap. For each patient in the two lung adenocarcinoma studies, personalized FAIME-profiles of GO biological processes are generated from genome-wide expression profiles. For both training studies, GO-signatures significantly associated to patient mortality were identified (Prediction Analysis for Microarrays; three-fold cross-validation). These two GO-signatures could effectively stratify patients from an independent validation cohort into sub-groups that show significant differences in disease-free survival (log-rank test P=0.019; P=0.001). Importantly, significant mechanism overlaps assessed by information-theory similarity were detected between the two GO-signatures (Fischer Exact Test p=0.001). Hence, together with machine learning technologies, FAIME could be utilized to develop an ontology-driven and expression-anchored prognostic signature that is personalized for an individual patient.

Resumo Limpo

aim provid clinic applic reproduc mechanist interpret gene express chang lack gene overlap among predict genesignatur use method recent develop function analysi individu microarray express faim provid evid gene ontologyanchor signatur gosignatur show reliabl prognosi lung cancer order demonstr biolog congruenc reproduc faimederiv mechan classifi chose diseas gene express classifi signatur alon fail signific stratifi larger collect sampl exhibit poor genet overlap patient two lung adenocarcinoma studi person faimeprofil go biolog process generat genomewid express profil train studi gosignatur signific associ patient mortal identifi predict analysi microarray threefold crossvalid two gosignatur effect stratifi patient independ valid cohort subgroup show signific differ diseasefre surviv logrank test p p import signific mechan overlap assess informationtheori similar detect two gosignatur fischer exact test p henc togeth machin learn technolog faim util develop ontologydriven expressionanchor prognost signatur person individu patient

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